Current AI research operates across incompatible paradigms — large language models, symbolic reasoning systems, world models, and perceptual encoders — each with representational logics that resist direct integration. Existing approaches (adapter layers, multimodal latent spaces, orchestration frameworks) either fail to scale or require retraining when new paradigms are added. This deposit proposes a conceptual alternative: frequency space as a universal codec for multi-paradigm AI integration. Rather than translating between paradigms, each paradigm independently encodes its representations as frequency signatures into a shared spectral space. The core structural claim is that frequency space has fixed orthogonality properties independent of any training objective — unlike trained latent spaces, whose geometry is paradigm-dependent by construction. The deposit contains three files: **File 1 — Paper (v0. 4): ** The formal working paper. Covers the integration problem, the frequency substrate proposal, its structural distinction from latent-space approaches, biological precedent (cochlear transform, neural oscillations), existing spectral AI architectures (FNet, GFNet, S4/Mamba), a connection to Holographic Reduced Representations for relational encoding, hardware alignment with photonic and neuromorphic computing, a minimal empirical pathway defining falsification conditions, and explicit scope and non-claims. **File 2 — Working Notes: ** The informal reasoning behind the architectural decisions. Documents why certain framings were adopted or rejected, what the open problems actually are (as opposed to how they appear in formal prose), and the process by which the framework developed. Published in the interest of transparency about AI-assisted conceptual development. **File 3 — Experiment Scripts: ** Three Python scripts implementing the minimal empirical pathway described in Section 8 of the paper: - `ufcₛeparabilityₑxperiment. py`: baseline separability test across noise levels, scale, and spectral resolution- `ufcₘultirunᵥalidation. py`: 10-seed validation confirming results are not initialization artifacts- `ufcwavetypeₑxperiment. py`: wave type comparison (FFT, DCT, Haar) and wavelength-reliability relationship **Key empirical results (toy scale): ** Separability holds perfectly under ideal conditions (cosine similarity = 1. 000 across all seeds). Degrades at noise σ=0. 05, fails at σ=0. 1. Scale is limited by signal length, not separability quality. Wave type (FFT, DCT, Haar) is not a significant factor — orthogonality is the relevant property. Lower frequency bands show measurably higher noise robustness, providing an empirical basis for band allocation as an architectural principle rather than a free parameter. This is a foundational conceptual framework and research agenda. Its value lies in the problem it defines and the architectural direction it proposes, not in what it demonstrates. Demolitions and extensions welcome. ADDITIONAL NOTES AI assistance (Claude, Anthropic) was used throughout the conceptual development of the ideas presented here. The framework originated in a pre-dawn conversation about the limits of the token paradigm. The working notes document this process in full. GitHub repository with experiment code: https: //github. com/JIJANERTITTARELLI/all-you-need-are-waves
Javier Ignacio Janer Tittarelli (Tue,) studied this question.